摘要
森林空间结构及动态变化规律对森林经营管理、生态环境建模等具有重要意义,无人机激光雷达与摄影测量能够获取丰富的森林空间结构和类型信息,在单木、林分尺度森林环境长时间序列监测方面具有无可比拟的优势。无人机激光雷达系统一般搭载多回波/全波形激光扫描仪,配备高精度全球导航卫星系统&惯性测量单元(GNSS&IMU)等传感器,以保证激光脉冲回波信号的几何定位精度。无人机摄影测量系统通常搭载可见光(RGB)/多光谱相机,配备低精度GNSS&IMU,通过高重叠率航片的三维重建算法自动解算航片内外方位元素,生成具有相对参考坐标的图像及点云,采用地面控制点(GCPs)、参考影像等方式进行几何精校正,对于连续覆盖的森林区域,使用高精度GNSS、稳定平台等可以提高图像匹配精度。通过单木分割法可以提取单木结构信息,从激光雷达点云或摄影测量重建点云中识别树冠顶点、树冠边界、位置等属性,也可以将点云投影到体元空间或者生成冠层高度模型(CHM),在此基础上识别单木特征。林分结构信息提取常采用高度分布法,从点云中直接计算高度分位数、回波指数等点云特征量,或者按照指定的高度间隔生成频率或强度合成波形,计算波形分位数、波形前沿、波形后沿等波形特征量,根据点云特征量、波形特征量与地面测量值之间的关系估测森林结构参数。激光雷达点云和摄影测量重建点云均能用于提取林下地形,对于低郁闭度区域二者相差不大,对于高郁闭度区域摄影测量重建点云提取的林下地形精度较低。多时相无人机激光雷达和摄影测量相结合,可以监测人工修枝、择伐、火灾、病虫害等引起的森林结构变化以及枝叶生长、落叶等物候变化。无人机激光雷达与摄影测量提取的森林结构参数精度受采集方式、数据处理算法、森林生长季节、地形�
Forest spatial structure and dynamics pattern are crucial to forest management and ecological modelling.Unmanned aerial vehicle（ UAV） based light detecting and ranging（ Li DAR） and photogrammetry could provide comprehensive spatial structure and species of forest,and have unrivalled advantages in the long-time monitoring of forest environment at individual tree or stand scale. UAV-based Li DAR system usually carries multiple echoes/full wave laser scanner,and assembles high precision global navigation satellite system（ GNSS） inertial measurement unit（ IMU）which is used to ensure the position accuracy of backscatter signals of transmitted laser pulses. UAV-based photogrammetry system mainly carries visual（ RGB）/multiband camera,and assembles low precision GNSS IMU. Automated 3D reconstruction algorithms can estimate the locations and orientations of cameras and camera internal parameters using highly overlapping aerial photographs,and generate initial rectified images and point cloud with relative coordinates,which can be georeferenced by ground control points（ GCPs）,reference images,etc. The accuracy of image matching canbe improved using high precision GNSS,stabilized platform,etc. Individual tree segmentation algorithms were generally used to extract structure information of individual trees,such as tree tops,crown edges,locations of trees,etc.,from point cloud of Li DAR or photogrammetry reconstruction. The structure features of individual trees can also be recognized from projected voxel space or canopy height model（ CHM） generated from point cloud. Forest stand structure information were usually estimated by height profile algorithms from point cloud or synthetic waveform. The point cloud can be directly used to calculate features,such as height percentile,echo index,etc.,or generate synthetic waveforms based frequency or intensity of echoes at specified bin of height. The waveform features,such as percentile,leading edge,trailing edge,etc.,can be extracted from synthetic wavefo
出处
《林业科学》
EI
CAS
CSCD
北大核心
2017年第7期134-148,共15页
Scientia Silvae Sinicae
基金
中国林业科学研究院中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2016SZ003)
国家重点基础研究发展计划(973计划)课题(2013CB733405
2013CB733404)
关键词
无人机
激光雷达
摄影测量
点云
森林
unmanned aerial vehicle（UAV）
light detection and ranging（Li DAR）
photogrammetry
point cloud
forest
